Skip to main content
SpringerLink
Log in

Optimization-Based Simulation of the Motion of a Human Performing a Horizontal Drop Jump

  • Conference paper
  • First Online:
Advances in Human Factors in Simulation and Modeling (AHFE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 591))

Included in the following conference series:

Abstract

The ‘Hybrid Predictive Dynamics Method for Digital Human Modeling’ is used in this work to analyze the dynamics of a human performing a “Drop Jump” task. The ‘Hybrid’ prefix mentioned in the literature recently [1] refers to the use of motion capture data for improving human motion simulations. This use of motion capture compensates for the inherent weaknesses of purely theoretical motion prediction due to deficiencies in computational power or available theoretical backgrounds. In this work, using motion capture data, an optimization based 3-D motion tracking of a human model performing a “Horizontal Drop Jump” is presented, as the first step in the simulation/prediction of such a motion. Based on the evaluation of the motion, dynamics properties of the motion are calculated which include ground reaction forces, joint torques and metabolic energy. The human model starts in a standing posture on top of a small box (low elevation from the ground), while jumping down the box, he changes his posture into a ready to jump pose with his two feet on the ground in-line and parallel to each other. After a brief recoil, he accelerates his center of gravity in the upward and forward direction and lifts off from the ground with an initial velocity in the same direction as his past acceleration. In the air, he will have a projectile motion and then he lands in his final position. The human model is a 55 degree of freedom (DOF) robot defined by Denavit-Hartenberg parameters. The base of the model is considered to be the hip point of a human. The orientation and position of this base in the global reference frame is defined by 6 DOF (3 position and 3 orientation). The robot includes 5 open-loop kinematic branches all originating from the base of the robot and ending in left hand, left foot, head, right hand and right foot. The remaining 49 DOF of the robot are revolute joints used in these 5 branches. Based on a motion capture set of data, motion is generated by a multi-objective optimization approach minimizing the difference between the location of markers on an actual human and the location of those same markers on the digital human model. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces are known. Therefore, it will be possible to calculate the total sum of ground reaction forces and moments. In the next step, joint torques are calculated using Lagrange’s equations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hariri, M.: The hybrid predictive dynamics method for analysis, simulation and prediction of human motion. In: ASME International Design Engineering Technical Conferences, vol. 6: 12th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Charlotte, North Carolina, USA, 21–24 August 2016 (2016)

    Google Scholar 

  2. Xiang, Y., Chung, H.J., Mathai, A., Rahmatalla, S., Kim, J., Marler, T., Beck, S., Yang, J., Arora, J.S., Abdel-Malek, K.: Optimization-based dynamic human walking prediction. In: SAE Digital Human Modeling Conference, June 2007, Seattle, WA (2007)

    Google Scholar 

  3. Hariri, M., Arora, J., Abdel-Malek, K.: Optimization-based prediction of aiming and kneeling military tasks performed by a soldier. In: ASME 2012 IDETC: Human Modeling and Simulation for Engineering, 12–15 August 2012, Chicago, IL, USA (2012)

    Google Scholar 

  4. Hariri, M., Arora, J., Abdel-Malek, K.: Optimization-based prediction of a soldier’s motion: stand-prone-aim transition task. In: New Trends in Mechanism and Machine Science: Theory and Applications in Engineering, pp. 459–467 (2013)

    Google Scholar 

  5. Hariri, M., Xiang, Y., Chung, H.J., Bhatt, R., Arora, J., Abdel-Malek, K.: Simulation and prediction of the motion of a human in a vertical jumping task. In: ASME 2013 Dynamic Systems and Control Conference, 21–23 October 2013, Stanford University, Palo Alto, CA (2013)

    Google Scholar 

  6. Marler, R., Rahmatalla, S., Shanahan, M., Abdel-Malek, K.: A new discomfort function for optimization-based posture prediction. SAE Technical Paper 2005-01-2680 (2005). doi:10.4271/2005-01-2680

  7. Gill, P.E., Murray, W., Saunders, M.A.: SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM J. Optim. 12, 979–1006 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Mahdiar, H.: A study of optimization-based predictive dynamics method for digital human modeling. Ph.D. Dissertation, The University of Iowa (2012)

    Google Scholar 

  9. Ackermann, M., van den Bogert, A.J.: Optimality principles for model-based prediction of human gait. J. Biomech. 43(6), 1055–1060 (2010)

    Article  Google Scholar 

  10. Bessonnet, G., Chesse, S., Sardain, P.: Optimal gait synthesis of a seven-link planar biped. Int. J. Robot. Res. 23(10–11), 1059–1073 (2004)

    Article  Google Scholar 

  11. Bessonnet, G., Seguin, P., Sardain, P.: A parametric optimization approach to walking pattern synthesis. Int. J. Robot. Res. 24(7), 523–536 (2005)

    Article  Google Scholar 

  12. Bessonnet, G., Marot, J., Seguin, P., Sardain, P.: Parametric-based dynamic synthesis of 3D-gait. Robotica 28(4), 563–581 (2010)

    Article  Google Scholar 

  13. Chevallereau, C., Aoustin, Y.: Optimal reference trajectories for walking and running of a biped robot. Robotica 19, 557–569 (2001)

    Article  Google Scholar 

  14. Davy, D.T., Audu, M.L.: A dynamic optimization technique for predicting muscle forces in the swing phase of gait. J. Biomech. 20(2), 187–201 (1987)

    Article  Google Scholar 

  15. Eriksson, A.: Optimization in target movement simulations. Comput. Methods Appl. Mech. Eng. 197, 4207–4215 (2008)

    Article  MATH  Google Scholar 

  16. Bottasso, C.L., Prilutsky, B.I., Croce, A., Imberti, E., Sartirana, S.: A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuromusculoskeletal system. Multibody Syst. Dyn. 16(2), 123–154 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  17. Capi, G., Yokota, M., Mitobe, K.: Optimal multi-criteria humanoid robot gait synthesis—an evolutionary approach. Int. J. Innov. Comput. Inf. Control 2(6), 1249–1258 (2006)

    Google Scholar 

  18. Leboeuf, F., Bessonnet, G., Seguin, P., Lacouture, P.: Energetic versus sthenic optimality criteria for gymnastic movement synthesis. Multibody Syst. Dyn. 16(3), 213–236 (2006)

    Article  MATH  Google Scholar 

  19. Marshall, R.N., Wood, G.A., Jennings, L.S.: Performance objectives in human movement: a review and application to the stance phase of normal walking. Hum. Mov. Sci. 8(6), 571–594 (1989)

    Article  Google Scholar 

  20. Vaughan, C.L.: Theories of bipedal walking: an odyssey. J. Biomech. 36(4), 513–523 (2003)

    Article  Google Scholar 

  21. Delp, S.L., Loan, J.P.: A computational framework for simulating and analysing human and animal movement. Comput. Sci. Eng. 2(5), 46–55 (2000)

    Article  Google Scholar 

  22. Fregly, B.J., Reinbolt, J.A., Rooney, K.L., Mitchell, K.H., Chmielewski, T.L.: Design of patient-specific gait modifications for knee osteoarthritis rehabilitation. IEEE Trans. Biomed. Eng. 54(9), 1687–1695 (2007)

    Article  Google Scholar 

  23. Lin, Y.C., Walter, J.P., Banks, S.A., Pandy, M.G., Fregly, B.J.: Simultaneous prediction of muscle and contact forces in the knee during gait. J. Biomech. 43(5), 945–952 (2010)

    Article  Google Scholar 

  24. Mahboobin, A., Cham, R., Piazza, S.J.: The impact of a systematic reduction in shoe-floor friction on heel contact walking kinematics-a gait simulation approach. J. Biomech. 43(8), 1532–1539 (2010)

    Article  Google Scholar 

  25. Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39(6), 1107–1115 (2006)

    Article  Google Scholar 

  26. Bobbert, M.F., Huijing, P.A., van Ingen Schenau, G.J.: Drop jumping. II. The influence of dropping height on the biomechanics of drop jumping. Med. Sci. Sports Exerc. 19, 339–346 (1987)

    Google Scholar 

  27. Lazaridis, S., Bassa, E., Patikas, D., Giakas, G., Gollhofer, A., Kotzamanidis, C.: Neuromuscular differences between prepubescents boys and adult men during drop jump. Eur. J. Appl. Physiol. 110, 67–74 (2010)

    Article  Google Scholar 

  28. Bassa, E.I., Patikas, D.A., Panagiotidou, A.I., Papadopoulou, S.D., Pylianidis, T.C., Kotzamanidis, C.M.: The effect of dropping height on jumping performance in trained and untrained prepubertal boys and girls. J. Strength Cond. Res. 26, 2258–2264 (2012)

    Article  Google Scholar 

  29. Holm, D.J., Stalbom, M., Keogh, J.W., Cronin, J.: Relationship between the kinetics and kinematics of a unilateral horizontal drop jump to sprint performance. J. Strength Cond. Res. 22, 1589–1596 (2008)

    Article  Google Scholar 

  30. Hunter, J.P., Marshall, R.N., McNair, P.J.: Relationships between ground reaction force impulse and kinematics of sprint-running acceleration. J. Appl. Biomech. 21, 31–43 (2005)

    Article  Google Scholar 

  31. Antonio, D.I., Martone, D., Milic, M., Johnny, P.: Vertical- vs. horizontal-oriented drop-jump training: chronic effects on explosive performances of elite handball players. J. Strength Cond. Res. (2016)

    Google Scholar 

  32. Kovacs, I., Tihanyi, J., Devita, P., Racz, L., Barrier, J., Hortobagyi, T.: Foot placement modifies kinematics and kinetics during drop jumping. Med. Sci. Sports Exerc. 31, 708–716 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas A&M University–Corpus Christi, 6300 Ocean Dr, Corpus Christi, TX, 78412, USA

    Mahdiar Hariri, Toyin Ajisafe & Jangwoon Park

Authors
  1. Mahdiar Hariri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toyin Ajisafe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jangwoon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdiar Hariri .

Editor information

Editors and Affiliations

  1. RDRL-HRF-D, United States Army, Aberdeen Proving Ground, Maryland, USA

    Daniel N. Cassenti

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Hariri, M., Ajisafe, T., Park, J. (2018). Optimization-Based Simulation of the Motion of a Human Performing a Horizontal Drop Jump. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2017. Advances in Intelligent Systems and Computing, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-60591-3_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60591-3_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60590-6

  • Online ISBN: 978-3-319-60591-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation